Fuzzy model identification by evolutionary, gradient based and memtic algorithms
One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some...
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| Other Authors: | , |
| Format: | conferenceObject |
| Language: | eng |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10400.1/2316 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2316 |
| Summary: | One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed. |
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